Face Recognition Windows SDK

Welcome to the MiniAiLive!

Check the likelihood that two faces belong to the same person. You will get a confidence score and thresholds to evaluate the similarity. Feel free to use our MiniAI Face Recognition Windows SDK.

Note

SDK is fully on-premise, processing all happens on hosting server and no data leaves server.

Table of Contents

• Installation Guide
• API Details
• Gradio Demo
• Python Test API Example

FaceSDK Installation Guide

Prerequisites

• Python 3.6+
• Windows
• CPU: 2 cores or more
• RAM: 8 GB or more

Installation Steps

1. Download the Face Recognition Windows Server Installer:

   Download the Server installer for your operating system from the following link:

   Download the On-premise Server Installer

2. Install the On-premise Server:

   Run the installer and follow the on-screen instructions to complete the installation.

   

3. Request License and Update:

   Run MIRequest.exe file to generate a license request file. You can find it here.

   C:\Program Files\MiniAiLive\MiniAiLive-FaceSDK-WinServer

   Open it, generate a license request file, and send it to us via email or WhatsApp. We will send the license based on your Unique Request file, then you can upload the license file to allow to use. Refer the below images.

   

4. Verify Installation:

   After installation, verify that the On-premise Server is correctly installed by checking the task manager:

   

FaceSDK API Details

Endpoint

• POST http://127.0.0.1:8083/api/face_detect Face Detection, Face Attributes API

• POST http://127.0.0.1:8083/api/face_detect_base64 Face Detection, Face Attributes API

• POST http://127.0.0.1:8083/api/face_match Face Matching API

• POST http://127.0.0.1:8083/api/face_match_base64 Face Matching API

Request

• URL: http://127.0.0.1:8083/api/face_detect
• Method: POST
• Form Data:
  • image: The image file (PNG, JPG, etc.) to be analyzed. This should be provided as a file upload.

• URL: http://127.0.0.1:8083/api/face_detect_base64
• Method: POST
• Raw Data:
  • JSON Format: { "image": "--base64 image data here--" }

Response

The API returns a JSON object with the recognized details from the input Face image. Here is an example response:

Gradio Demo

We have included a Gradio demo to showcase the capabilities of our Face Recognition SDK. Gradio is a Python library that allows you to quickly create user interfaces for machine learning models.

How to Run the Gradio Demo

1. Install Gradio:

   First, you need to install Gradio. You can do this using pip:

   git clone https://github.com/MiniAiLive/FaceRecognition-Windows.git
   pip install -r requirement.txt
   cd gradio

2. Run Gradio Demo:

   python app.py

Python Test API Example

To help you get started with using the API, here is a comprehensive example of how to interact with the Face Recognition API using Python. You can use API with another language you want to use like C++, C#, Ruby, Java, Javascript, and more

Prerequisites

• Python 3.6+
• requests library (you can install it using pip install requests)

Example Script

This example demonstrates how to send an image file to the API endpoint and process the response.

import requests

# URL of the web API endpoint
url = 'http://127.0.0.1:8083/api/face_detect'

# Path to the image file you want to send
image_path = './test_image.jpg'

# Read the image file and send it as form data
files = {'image': open(image_path, 'rb')}

try:
    # Send POST request
    response = requests.post(url, files=files)

    # Check if the request was successful
    if response.status_code == 200:
        print('Request was successful!')
        # Parse the JSON response
        response_data = response.json()
        print('Response Data:', response_data)
    else:
        print('Request failed with status code:', response.status_code)
        print('Response content:', response.text)

except requests.exceptions.RequestException as e:
    print('An error occurred:', e)

Request license

Feel free to Contact US to get a trial License. We are 24/7 online on WhatsApp.

Face & IDSDK Online Demo, Resources

Our Products

No	Project	Features
1	FaceRecognition-Docker	1:1 & 1:N Face Matching
2	FaceRecognition-Windows	1:1 & 1:N Face Matching
3	FaceRecognition-Linux	1:1 & 1:N Face Matching
4	FaceRecognition-LivenessDetection-Android	1:1 & 1:N Face Matching, 2D & 3D Face Passive Liveness Detection
5	FaceRecognition-LivenessDetection-iOS	1:1 & 1:N Face Matching, 2D & 3D Face Passive Liveness Detection
6	FaceRecognition-LivenessDetection-CPP	1:1 & 1:N Face Matching, 2D & 3D Face Passive Liveness Detection
7	FaceLivenessDetection-Docker	2D & 3D Face Passive Liveness Detection
8	FaceLivenessDetection-Windows	2D & 3D Face Passive Liveness Detection
9	FaceLivenessDetection-Linux	2D & 3D Face Passive Liveness Detection
10	FaceLivenessDetection-Android	2D & 3D Face Passive Liveness Detection
11	FaceLivenessDetection-iOS	2D & 3D Face Passive Liveness Detection
12	FaceAttributes-Android	Face Attributes, Age & Gender Estimation
13	FaceMatching-Android	1:1 Face Matching
14	FaceMatching-WinApp	1:1 Face Matching
15	ID-DocumentRecognition-Docker	ID Card, Passport, Driver License, Credit Card, MRZ Recognition
16	ID-DocumentRecognition-Windows	ID Card, Passport, Driver License, Credit Card, MRZ Recognition
17	ID-DocumentRecognition-Linux	ID Card, Passport, Driver License, Credit Card, MRZ Recognition
18	ID-DocumentRecognition-Android	ID Card, Passport, Driver License, Credit Card, MRZ Recognition
19	ID-DocumentLivenessDetection-Docker	ID Document Liveness Detection
20	ID-DocumentLivenessDetection-Windows	ID Document Liveness Detection
21	ID-DocumentLivenessDetection-Linux	ID Document Liveness Detection

About MiniAiLive

MiniAiLive is a leading AI solutions company specializing in computer vision and machine learning technologies. We provide cutting-edge solutions for various industries, leveraging the power of AI to drive innovation and efficiency.

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